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import pandas as pdimport foliumfrom pathlib import Pathimport altair as altimport plotly.io as pioimport plotly.express as pximport numpy as npimport matplotlib.pyplot as plt
With about 32.6% sidewalks and no visible roadway, the image shows a wide, brick-paved pedestrian space with benches and active foot traffic, indicating high walkability and comfort. Roughly 21.1% trees plus additional planting beds provide good shade and greenery, while substantial, well-maintained building facades create a strong, pleasant sense of enclosure.
Total Score: 81
Sidewalk score
22
Greenery score
18
Enclosure score
21
Comfort score
20
Comment
With about 39.9% sidewalks and almost no visible roadway, the image shows a wide, continuous, unobstructed sidewalk along active ground-floor shops, which supports good walkability and enclosure. Around 22% trees provide a pleasant canopy and some shade, and the limited roadway presence plus street lighting and clear sightlines contribute to a comfortable, safe-feeling pedestrian environment.
Total Score: 81
Sidewalk score
21
Greenery score
20
Enclosure score
19
Comfort score
21
Comment
With about 28% of the scene as paved pedestrian space and no visible roadway, the brick sidewalks and plaza appear wide, continuous, and well-separated from traffic, supporting comfortable walking. Roughly 27% trees plus dense canopy provide shade and greenery, while the historic, well-maintained building facades create a defined, pleasant enclosure that feels safe and calm despite some visual clutter from posts and chains.
Total Score: 80
Sidewalk score
21
Greenery score
18
Enclosure score
22
Comfort score
19
Comment
With about 18.1% sidewalks and only 11.5% roadway, the image shows a wide, unobstructed sidewalk along a calm residential street. Roughly 26.6% trees plus landscaped shrubs provide good shade and greenery, while the well-maintained, continuous building facade offers strong enclosure and a comfortable, safe-feeling environment.
Total Score: 78
Sidewalk score
21
Greenery score
18
Enclosure score
20
Comfort score
19
Comment
With about 17.6% sidewalks and a visibly wide, continuous, unobstructed pedestrian zone, walkability is strong, supported by active storefronts and street furniture. Roughly 20.7% trees and additional planters provide good shade and greenery, while moderate roadway share (17.6%) and calm traffic conditions contribute to a comfortable, enclosed, and safe-feeling streetscape.
Segmentation shows very little sidewalk (1.1%) and roadway (0.6%), which matches the image of a narrow underpass with a constrained, elevated sidewalk and no clear pedestrian crossings or amenities. With almost no greenery (0% trees/grass) and harsh, tunnel-like walls providing enclosure but also a dark, vehicle-oriented environment, overall comfort and perceived safety for walking are low.
Total Score: 20
Sidewalk score
3
Greenery score
1
Enclosure score
10
Comfort score
6
Comment
With 0% sidewalks in the segmentation and visible construction barriers, cones, and highway-like conditions, this area offers very poor walkability and feels dominated by cars. The 20.9% building facades provide some enclosure but are set back and separated by wide roadway (12.1%) and work zones, while greenery is almost absent and the environment appears exposed and uncomfortable for pedestrians.
Total Score: 21
Sidewalk score
6
Greenery score
0
Enclosure score
8
Comfort score
7
Comment
With only about 1.4% sidewalk and a wide, vehicle-oriented gas station forecourt, walking space is minimal, broken up by driveways, and dominated by parked and moving cars. The scene has virtually no trees or grass (0% greenery) and limited building frontage (13.3% facades), so enclosure and comfort are low despite clear visibility and daylight.
Total Score: 22
Sidewalk score
2
Greenery score
10
Enclosure score
6
Comfort score
4
Comment
Segmentation shows 22.4% roadway and 0% sidewalks, which matches the image of a narrow curb edge directly beside a blank wall with no usable pedestrian path. While there is substantial tree coverage (31.2%) hanging over the wall providing some greenery, the high, featureless concrete wall, lack of active frontages, and car-oriented roadway make the space feel uncomfortable and unsafe for walking.
Total Score: 23
Sidewalk score
3
Greenery score
6
Enclosure score
8
Comfort score
6
Comment
With only about 1.9% sidewalks and a visually narrow, obstructed edge behind parked cars and barriers, walkability is very poor. Limited trees (6.4%) and some tall but worn buildings and infrastructure create a harsh, car-dominated environment that feels exposed and not very comfortable for pedestrians.
I want to end by going back to the basic question of this project. On a map a street can look very walkable. From the sidewalk it can feel very different. My goal was to see whether AI can help us measure that feeling along three real streets in Philadelphia.
To do this I followed three simple steps. First, I built a regular 250 meter sampling along Market Street, Chestnut Street and Walnut Street and collected street view images for each point. Second, I used semantic segmentation to break each image into basic elements and to calculate the share of sidewalk, roadway, buildings, sky and greenery. Third, I asked a large language model to look at each scene and to give four scores for sidewalk quality, greenery, enclosure and comfort and safety, which sum to a total walkability score from 0 to 100.
The results are not perfect, but they make sense. Higher sidewalk and greenery shares usually come with higher walkability scores. Large exposed roadway often comes with lower comfort and safety. On the maps, many of the lowest scores appear at places like bridge ramps, underpasses and gas stations. These are exactly the spots that also feel unpleasant or unsafe to us as pedestrians.
For me the main message is that AI already has enough perceptual ability to do a reasonable first pass on streetscape walkability. It can read visual cues in a way that is similar to human judgment. It can also do this very quickly and at scale, across hundreds or thousands of scenes, which was very hard with manual ratings or custom models in the past.
This does not mean AI replaces fieldwork or community input. It still has biases and blind spots and it does not know local context. But it can be a useful screening tool that helps us see patterns along long corridors and identify segments that deserve closer attention.
Looking ahead, I think the more interesting question is not only whether AI can see the street, but how we choose to use that ability. If we combine AI’s fast, city scale perception with human experience and community voices, we can move closer to designing streets that are not only “walkable” on paper, but actually feel walkable to the people who use them every day.